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Uncorrelated component analysis for blind source separation

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Abstract

The uncorrelated component analysis (UCA) of a stationary random vector process consists of searching for a linear transformation that minimizes the temporal correlation between its components. Through a general analysis we show that under practically reasonable and mild conditions UCA is a solution for blind source separation. The theorems proposed in this paper for UCA provide useful insights for developing practical algorithms. UCA explores the temporal information of the signals, whereas independent component analysis (ICA) explores the spatial information; thus UCA can be applied for source separation in some cases where ICA cannot. For blind source separation, combining ICA and UCA may give improved performance because more information can be utilized. The concept of single UCA (SUCA) is also proposed, which leads to sequential source separation.

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Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, People's Republic of China

    Chunqi Chang, Paul Kwok, Francis H. Y. Chan & F. K. Lam

  2. Department of Electrical and Electronic Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, People's Republic of China

    Sze Fong Yau

Authors
  1. Chunqi Chang
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  2. Sze Fong Yau
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  3. Paul Kwok
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  4. Francis H. Y. Chan
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  5. F. K. Lam
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Additional information

This work was supported in part by grants from the Research Grants Council of Hong Kong, grants HKU553/96M, HKU7036/97E, and HKUST776/96E.

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Chang, C., Yau, S.F., Kwok, P. et al. Uncorrelated component analysis for blind source separation. Circuits Systems and Signal Process 18, 225–239 (1999). https://doi.org/10.1007/BF01225696

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  • DOI: https://doi.org/10.1007/BF01225696

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